Noninvasive approaches to detect methylation-based markers to monitor gliomas

Abstract In this review, we summarize the current approaches used to detect glioma tissue-derived DNA methylation markers in liquid biopsy specimens with the aim to diagnose, prognosticate and potentially track treatment response and evolution of patients with gliomas.


Overview
Malignant transformation and/or therapeutic resistance causes pervasive relapse of glioma subtypes, leading to high death rates after the current treatment protocols (maximum surgical excision followed by chemoradiotherapy). 1 Longitudinal monitoring of glioma evolution is currently performed through serial clinical and conventional magnetic resonance imaging (MRI) protocols. 2 However, conventional or advancing imaging techniques, in addition to being costly and not available or feasible for some patients, this approach lacks the sensitivity or specificity required to diagnose minimal residual tumor burden or discriminate true recurrence from necrosis or pseudoprogression. [2][3][4][5][6] The final diagnosis of true progression, for instance, relies on histological assessment of tumor tissue obtained through surgical interventions which may not be clinically viable for some patients, resulting in potential postponements in the administration of salvage therapies. 7 Furthermore, serial surgical procedures are not suitable for the detection of glioma heterogeneity or surveillance of the dynamic morphological and molecular changes frequently encountered during glioma evolution [8][9][10] .
Liquid biopsy has emerged as an attractive non-or minimally invasive approach to complement and overcome some of the challenges and limitations associated with surgical and imaging procedures in real-time detection and monitoring of glioma evolution. [11][12][13] This approach involves the detection of tumor cells, extracellular vesicles (e.g. exosomes), and molecular elements fragments (e.g. nucleic acids) released by tumors into biofluids such as blood, cerebrospinal fluid (CSF), and urine. 11 Cell-free DNA (cfDNA), particularly the tumor fraction (ctDNA), one of the molecular elements most frequently assessed in liquid biopsy specimens, is amenable to quantitative analysis and molecular interrogation through different epi(genetic) survey platforms. 11,12,14 Among the epigenetic mechanisms that play key roles in glioma development and progression, DNA methylation abnormalities are the most frequently reported and are readily detected in liquid biopsy specimens. 12,[14][15][16] Interrogation of DNA methylation abnormalities using liquid biopsy specimens is advantageous in gliomas and other CNS tumors as they are pervasive and recurrent within tumor types, affect the entire genome, allow for the detection of methylation patterns or markers that backtrack to the cell-of-origin and highlight potential therapeutic targets, thus outperforming the profiling of somatic mutation sequencing for the diagnosing and monitoring of cancer. [16][17][18][19][20][21][22] In the present review, we summarize the current approaches used to detect glioma tissue-derived DNA methylation markers in liquid biopsy specimens with the aims to diagnose, prognosticate and potentially track treatment response and glioma evolution/progression.

Noninvasive approaches to detect methylation-based markers to monitor gliomas ii23
Noushmehr et al. Liquid biopsy and epigenetics

Tissue Specimens
Since 2016, the World Health Organization (WHO) recommended integration of molecular features to the classification of CNS tumors such as the mutational status of the isocitrate dehydrogenase (IDH) genes to segregate gliomas into IDH-mutant and IDH-wildtype subtypes, which present prognostic value. 23 Interestingly, IDH-mutant gliomas manifest the cytosine-phosphate-guanine (CpG) island methylator phenotype (G-CIMP) and have generally more favorable prognosis than their IDH-wildtype counterparts. 16,24,25 IDH-mutants may be further stratified into G-CIMP-high and G-CIMP-low subsets according to higher or lower genome-wide methylation levels, respectively. 16,24,26,27 Unlike patients with G-CIMP-high gliomas, those affected by G-CIMP-low tumors have a poorer prognosis and lower methylome levels, which are also observed in patients carrying IDH-wildtype gliomas. 16,24,26,27 Studies involving the longitudinal analysis of tissue methylomes observed a subset of G-CIMP-high glioma which loses methylation at recurrence and acquires a G-CIMP-low phenotype associated with a more aggressive behavior. 26,27 Comparison of the methylomes of G-CIMP-high gliomas which progress to G-CIMP-low phenotype to those which retain their high methylation status at first recurrence yielded the identification of a set of methylation signatures able to predict risk of progression to a more aggressive subtype across independent cohorts (namely glioma prognostic classifier). 26,27 In a recent longitudinal study involving homogeneously treated IDH-mutant anaplastic astrocytomas, now referred to as Astrocytoma, IDH-mutant, WHO Grade 3, the authors confirmed that patients classified as G-CIMP-low at diagnosis or recurrence presented worse overall survival than their G-CIMP-high counterparts. 27 Analysis of tissue methylomes across low-and highgrade diffuse gliomas led to the identification of DNA methylation groups presenting distinct clinicopathological, genomic, and prognostic features. 15,16,24 The identification of methylation signatures specific to each subtype allowed for the development of a machine learning-based model, namely the glioma methylation subtype classifier, which exhibited high accuracy in stratifying an independent cohort according to specific methylation groups. 16,26,27 Given that genome-wide DNA tissue methylation patterns are conserved across cell and tumor types that originate from a common lineage, the profiling of this molecular feature constitutes a reliable and reproducible approach to detect methylation fingerprints to diagnose and classify tumors, including those of the CNS. 12,13,20,[28][29][30] Based on CNS tumor tissue methylome signatures, a machine learningbased classifier was able to, with high accuracy, discriminate across and within tumor types, including gliomas, and has been incorporated into the 2021 edition of the World Health Organization CNS tumor classification. 27,29,30 The use of this DNA methylation-based classifier has shown to be especially advantageous in distinguishing tumors with unusual histopathological features or specimens with small amounts of tumor. 30,31 In summary, detection of glioma-relevant DNA methylation markers has the potential to change diagnostic, prognostic, and patient-monitoring paradigms.

Methylation Markers in Glioma Liquid Biopsy Specimens
Studies have shown that methylation profiling of cellfree DNA released from CNS tumors in blood (serum or plasma) and other biosources (circulating tumor cells, extracellular vesicles, urine and CSF) allows for the detection of tumor-specific molecular markers [11][12][13][14]28 (Tables  1 and 3).
One of the main hindrances to the application of liquid biopsy in the management of patients with CNS tumors, particularly gliomas, is the minute amount of cellular and molecular elements released by these tumors into biofluids and the obtention of good quality DNA for downstream molecular profiling. Currently, there are no standardized methodologies to detect and profile these molecular elements, and the sensitivity of current methods is highly variable. 11,14,32,33 However, ongoing technological advances show promise in improving the accuracy of these methods [11][12][13][14]34,35 (Tables 1 and 3). A summary of studies reporting on the application of DNA methylationbased liquid biopsy studies in patients with gliomas is displayed in Tables 1 and 3.

Assessment Approaches for cf-and ctDNA Analysis
Circulating cell-free DNA (cfDNA) originates from healthy and neoplastic cells as a result of necrosis, apoptosis of nucleated cells, and/or active secretion. 36 Interestingly, measurement of total plasma cfDNA concentration, i.e., a combination of both tumor-and nontumor-derived cfDNA, has shown clinical relevance. For instance, studies revealed that patients with high-grade glioma had significantly higher plasma cfDNA concentration compared to patients with low-grade glioma at initial diagnosis or healthy controls. 37,38 In a pilot prospective study, 37 the authors showed that the pre-surgical concentration of glioblastoma-derived plasma cfDNA was associated with lower progression-free survival rates (PFS); additionally, the cfDNA level correlated with radiological tumor burden and increased during tumor progression, after radiation therapy.
An important challenge in the molecular downstream analysis of total cfDNA is discriminating between ctDNA signals from the nontumor-derived cfDNA. 14,39 In response, several strategies to enhance the detection sensitivity of the ctDNA fraction in plasma or serum have been developed such as the detection of knowingly tumor-specific gene mutations, copy number alterations or methylation signatures characteristic of each tumor type in cfDNA specimens 14,19,22,40 or the profiling of cfDNA fragments (fragmentomics) to select lengths characteristic of ctDNA in plasma, urine, or CSF. The latter may be followed by downstream genomic or epigenomic molecular analysis as described in gliomas and other tumors. 39

cfDNA Methylation Profiling Methods
The description, advantages, and limitations of DNA methylation-based and other methods applied to liquid biopsy specimens is reviewed elsewhere 14,39 and summarized in Table 2.

5-Methylcytosine (5mc) and 5-Hydroxymethylcytosine (5hmC) Modification Profiling
The screening of 5-methylcytosine (5mc) and 5-hydroxymethylcytosine (5hmC, a demethylation marker resulting from 5mC oxidation) in liquid biopsy specimens holds great potential for early detection, diagnosis, prognosis, and monitoring of the dynamic changes and treatment outcomes in cancers, specifically gliomas 38,[44][45][46][47][48] High-throughput genome-wide (whole genome bisulfite sequencing [WGBS], reduced representation bisulfite sequencing [RRBS], sequence only genomic regions rich in CpG dinucleotides), and microarray assays are the most commonly reported sequencing methods to profile genomic or liquid biopsy-derived DNA. These methods indistinctly detect both 5mC and 5hmC modifications; however, adjustments such as simultaneous bisulfite and oxidative bisulfite treatments allow their resolution. 14,48 Bisulfitebased methods provoke a significant degradation of DNA (over 90%) which may be detrimental to the detection of the low-input cfDNA amount released by CNS tumors. 14,40 Adaptations of these methods or bisulfite-free sequencing protocols (e.g. chemical-or enzymatic-based and affinity enrichment methods) were developed to circumvent this limitation and to improve the detection of low-input DNA from tissue and liquid biopsy specimens. 14,48 The methylated DNA immunoprecipitation (MeDIP) approach, for instance, is based on affinity enrichment with 5-methylcytosine (5mC) or 5hmC-antibodies followed by high-throughput sequencing of genomic (tissue) or cfDNA, namely MeDIP (Methylated DNA Immunoprecipitation) and cfMeDIP-seq, respectively and is efficient to detect small amount of DNA in tissue or liquid biopsy specimens. The application of cfMeDIP-seq to plasma samples was able to detect and discriminate across intracranial tumors, including gliomas. 13,28 Another approach that has shown diagnostic value in gliomas is the specific profiling of the levels of genome-wide 5hmc, a cytosine modification with gene transcription regulatory role and tissue-specificity associated with the diagnosis and prognostication of many tumors. 38,49,50 In a study, genome-wide 5hmc profiling using a highly sensitive and robust chemical labeling technique (5hmC-Seal technique) in plasma samples from patients with gliomas showed that 5hmc levels were higher in gliomas compared to healthy controls and several machine learning-based models using 5hmc concentration as input were able to discriminate gliomas from healthy controls and glioblastomas (grade 4). 38 Methylated Glioma-Specific DNA Detection The feasibility of detecting methylated genes presenting biological or clinical relevance using targeted methods (methyl-specific or droplet digital PCR) or multiple regions of interest (target sequencing) in cfDNA specimens has also been reported (Table 2). 51 For instance, in addition to its reported prognostic and predictive values, studies have shown that the serial detection of MGMT promoter methylation abnormalities in plasma or serum cfDNA from patients with gliomas was associated with tumor burden after treatment and with progression 14,52,53 Application of cfDNA Methylation-Based Machine-Learning Algorithms cfDNA The application of computational methods, particularly machine-learning (Figure 1), to analyze the large array of molecular information generated through highthroughput omics data, constitutes a robust approach towards identification of valuable biomarkers for tumor diagnosis and prognostication and requires specialized algorithms, in addition to the ones used for tumor tissue, for liquid biopsy specimens. For instance, based on DNA methylation data obtained through targeted or genome-wide profiling of tumor-derived tissue or liquid biopsy specimens, studies reported on machine learning algorithms able to estimate tumor load, cell-of-origin, molecular subtypes, or prognosis (Table 3). 12,13,19,22,[54][55][56] Specifically to gliomas, machine learning models using cfDNA epigenetic markers profiled in blood (serum or plasma) or urine specimens showed greater than 80% accuracy in diagnosing these tumors, recapitulating the diagnostic accuracy obtained using tissue-derived methylation markers from glioma. 12,42,57

Final Remarks
Here we summarize the results of studies that used minimally or noninvasive liquid biopsy approaches to detect methylation-based markers to diagnose and prognosticate gliomas and explore the feasibility of some methods to monitor tumor evolution and treatment in real time. Although promising, there remains a need for standardized operating procedures involving the pre-analytical factors (biosource type, molecular profiling technologies, and data analysis strategies) as well as validation in larger cohorts and prospective designed studies before moving the application of this approach into clinical practice. 11,14,58 Ongoing advancements in technologies and strategies involved in the isolation, detection and data analysis have shown improvements in the identification of relevant methylation markers using the minute amounts of cellular and molecular elements released by CNS tumors and in reproducibility of DNA methylation-based cfDNA analysis. Some companies are also developing assays that simultaneously assess different combinations of analytes in ctDNA such as fragmentomics and nucleosome positioning, next-generation sequencing, and immunoassays. 11,14,58 Consortia in the liquid biopsy field such as the "Brain-Liquid Biopsy consortium" , established in 2020, Step #3.

External Model Application
Step #1. Appropriate Algorithm Selection

Neuro-Oncology Advances
has the potential to mirror the achievements of The Cancer Genome Atlas (TCGA) and ongoing Glioma Longitudinal AnalySiS (GLASS) consortia 10 facilitating the standardization of procedures and the generation of reliable molecular and imaging data from large cohorts of patients.

Future Perspectives
Development of a user-friendly web-based platform to upload molecular and clinical data extracted from liquid biopsy specimens followed by automatic normalization, random forest classification using methylation markers, and PDF report generation regarding tumor subtyping and prognosis is crucial and has the potential to mirror the success of web platforms developed for tumor tissue analysis, 20 This proposed platform will allow sharing data and facilitate fast and secure communication between researchers, physicians, and patients. Global access to this web-based platform will further expand the number of patients profiled and accelerate the efforts towards its validation. The success of these efforts will ultimately result in the much needed enhancement in treatment opportunities and improvement of quality of life for patients with brain tumors.

Funding
This work was financially supported by the Henry Ford Health System, Department of Neurosurgery internal grant.
Conflicts of interest statement. The authors report no conflicts of interest.